Design of Dynamic Tracking Filter for Model Based Diagnosis of Gas Turbine Engine

There exist many approaches for gas turbine engine condition monitoring and fault diagnosis. Among them, gas path analysis depends on the relations between deviations of performance parameters and deviations of measurements, such as pressure, temperature, at some positions in the flow path. In the first author’s previous study, a dynamic tracking filter is combined with a nonlinear gas turbine model to form a fault diagnosis system. The dynamic tracking filter is composed with multiple negative feedback control loops in which the residuals between model outputs and measurements are driven to zero by adjusting the performance parameters. In the present study, an interaction analysis technique, named the Relative Gain Analysis (RGA), is introduced to design more convincing and formal tracking filter for a heavy-duty gas turbine diagnostic problem. The basic concept of the RGA method is introduced in this paper with a simple blending example. Then a gas turbine model built using the Simscape language and environment from the MathWorks Co. is presented. The effects of secondary air system on the performance of compressor and turbine are considered in this gas turbine model. The linear influence coefficient matrix for four performance parameters and four measurement parameters is obtained from the steady state simulation with proper disturbance of performance parameters. Then the relative gain matrix (RGM) is obtained from matrix operation. To evaluate the pairing rule proposed in the RGA method, four tracking loops are built up to form a tracking filter for the four performance parameters selected. Deviations of performance parameters are implanted into the gas turbine model by adjusting the scaling factors of performance maps; and then simulation results are taken as measurements needed for the tracking filter to run. Tracking results of performance parameters in different cases are given to show the tracking capability for isolated performance deviations and concurrent performance deviations.